Abstract: A content recommendation method and system are described, according to various implementation. In an implementation, the method and system generate a record identifying a set of content items interacted with by users of a content system, where each content item is associated with one or more topics. A confidence score associated with each of a set of topics associated with the set of content items is generated, where the confidence score represents a quantity of instances a respective topic appears in a collection of content items. A portion of the set of topics having a confidence score that exceeds a threshold confidence level is determined. A search of the collection of content items is executed to identify a serendipitous candidate content item that includes multiple portions of the set of topics previously interacted with by a user. The method and system determines, based on the record, that the candidate serendipitous content item has not been interacted with by the user system.

Abstract: A personalized content recommendation provisioning method and system are described, according to various implementations. A token value allocation engine optimizes the allocation of content consumption revenue to publisher systems and recommendation source systems (e.g., the systems that provide the recommended content for presentation with the publisher system's native content) based on a model calculating and applying a life time or long term value (LTV) metric. The LTV metric for revenue is based on a detection of a how often a user system engages with content derived from or associated with a publisher system.

Abstract: Additional systems and methods for curating content. In one embodiment, a method for providing user access to online content can be provided. The method can include providing a user access to aggregated content from one or more third party webpages, wherein the aggregated content is stored in at least one data storage device, receiving a user selection of a portion of the aggregated content, and posting a link to the user selected portion of aggregated content on a webpage associated with the user, wherein the link is to a second webpage associated with the user.

Abstract: A personalized content recommendation provisioning method and system are described, according to various implementations. In an implementation, a user profile is established for each of multiple users within an electronic data environment (e.g., the Internet). The user profile may include information collected via a registration of the user and information identifying content consumed by the user based at least in part on information collected by an associated tracking cookie. The user profile may be used to generate a personalized grade associated with each of multiple candidate content recommendations in a high-quality candidate content recommendation pool, determine a content recommendation scope associated with a user in view of a personalized grade associated with each of the plurality of candidate content recommendations, and display the content recommendation scope to the user.

Abstract: Systems and methods for curating content. In one embodiment, a method for curating content from a set of third party webpages can be provided. The method can include aggregating content from the third party webpages, receiving an indication that content from one or more of the third party webpages has been selected, receiving an indication of a selected interface position for at least a portion of the selected content, and outputting an interface with the portion of selected content in the selected interface position.

Abstract: A method and system for generating content title recommendations for content titles associated with a content page is disclosed. The method and system collects user activity data representing user engagement levels relating to multiple content webpages, wherein each content page is associated with a content title. A title replacement candidate is identified in view of the collected user activity data, wherein the title replacement candidate includes a plurality of title components. The title replacement candidate is compared to one or more high user engagement value titles. Based on the comparison, one or more high user engagement title component recommendations are identified which correspond to one or more of the title components of the title replacement candidate.

Abstract: Identifying personalized content recommendations for users in an electronic environment is disclosed. User data comprising information relating to web-based content consumption of multiple users is collected. Multiple user cluster types associated with one or more interest categories are established. A feature vector is generated for each user for each of the multiple user cluster types. Based on the generated feature vectors, the user are grouped into multiple clusters. A grade is generated for each of a plurality of candidate recommendations for each of the clusters. Based on the generated grades, one or more personalized content recommendations for each of the clusters are identified.

Abstract: A server computer receives data indicating a data feed to use to create a feed-based advertisement to represent content, which is to be advertised, of a content provider. The server computer receives the data feed that is associated with the content to be advertised and generates the feed-based advertisement using one or more data elements associated with an item in the data feed. The server computer provides the feed-based advertisement to a user device.

Abstract: A server computer system identifies a user and a destination document. The server computer system identifies recommendations that correspond to the user and the destination document. The server computer system determines grades for the recommendations based on the user and/or the destination document. The server computer system determines serving probabilities for the recommendations based on the proportions of the grades and provides the serving probabilities to serve the recommendations.

Abstract: A server computer system receives campaign data from one or more content providers. The server computer system determines assignments of recommendation opportunities in documents to recommendations based on the campaign data. The server computer system ranks the recommendations based on the assignments, allocates the recommendations to the documents based on the rankings of the recommendation opportunities, and determines pricing for the recommendations based on the allocations.

Abstract: Embodiments of the invention can provide systems and methods for identifying and measuring trends in consumer content demand within a vertical network of websites and related content. In one embodiment, a method can include receiving content from webpages in at least one vertical; receiving data associated with a plurality of selected keywords associated with the at least one vertical, wherein one or more associations between at least a portion of the plurality of selected keywords can be generated; receiving session data associated with a plurality of consumers accessing content in webpages in the at least one vertical; determining third party data associated with the plurality of consumers accessing at least a portion of webpages comprising at least one of the selected keywords; and aggregating, based at least in part on the third party data, session data associated with visits to the webpages comprising at least one of the selected keywords.

Abstract: A personalized content recommendation provisioning method and system are described, according to various implementations. In an implementation, a user profile is established for each of multiple users within an electronic data environment (e.g., the Internet). The user profile may include information collected via a registration of the user and information identifying content consumed by the user based at least in part on information collected by an associated tracking cookie. The user profile may be used to generate a personalized grade associated with each of multiple candidate content recommendations in a high-quality candidate content recommendation pool, determine a content recommendation scope associated with a user in view of a personalized grade associated with each of the plurality of candidate content recommendations, and display the content recommendation scope to the user.

Abstract: Identifying impressions relating to a target publisher that are related to or derived from a user interaction with a content recommendation source. User activity data for multiple users is collected during an activity window. Based on the collected user activity data, an initial interaction by a user with a source is identified and used to establish a source-related user session beginning at a time of the initial interaction and ending after a session period. A set of impressions (e.g., page views) by the user relating to the target publisher occurring during the user session is identified. The identified set of impressions is associated with the user session. A source-related user activity measurement is calculated based on the identified user sessions and associated impressions occurring during the activity window.